ETS1, a Target Gene of the EWSR1::FLI1 Fusion Oncoprotein, Regulates the Expression of the Focal Adhesion Protein TENSIN3.

The mechanistic basis for the metastasis of Ewing sarcomas remains poorly understood, as these tumors harbor few mutations beyond the chromosomal translocation that initiates the disease. Instead, the epigenome of Ewing sarcoma cells reflects the regulatory state of genes associated with the DNA-binding activity of the fusion oncoproteins EWSR1::FLI1 or EWSR1::ERG. In this study, we examined the EWSR1::FLI1/ERG's repression of transcription factor genes, concentrating on those that exhibit a broader range of expression in tumors than in Ewing sarcoma cell lines. Focusing on one of these target genes, ETS1, we detected EWSR1::FLI1 binding and an H3K27me3-repressive mark at this locus. Depletion of EWSR1::FLI1 results in ETS1's binding of promoter regions, substantially altering the transcriptome of Ewing sarcoma cells, including the upregulation of the gene encoding TENSIN3 (TNS3), a focal adhesion protein. Ewing sarcoma cell lines expressing ETS1 (CRISPRa) exhibited increased TNS3 expression and enhanced movement compared with control cells. Visualization of control Ewing sarcoma cells showed a distributed vinculin signal and a network-like organization of F-actin; in contrast, ETS1-activated Ewing sarcoma cells showed an accumulation of vinculin and F-actin toward the plasma membrane. Interestingly, the phenotype of ETS1-activated Ewing sarcoma cell lines depleted of TNS3 resembled the phenotype of the control cells. Critically, these findings have clinical relevance as TNS3 expression in Ewing sarcoma tumors positively correlates with that of ETS1. Implications: ETS1's transcriptional regulation of the gene encoding the focal adhesion protein TENSIN3 in Ewing sarcoma cells promotes cell movement, a critical step in the evolution of metastasis.

The peptide GOLVEN10 alters root development and noduletaxis in Medicago truncatula.

The conservation of GOLVEN (GLV)/ROOT MERISTEM GROWTH FACTOR (RGF) peptide encoding genes across plant genomes capable of forming roots or root-like structures underscores their potential significance in the terrestrial adaptation of plants. This study investigates the function and role of GOLVEN peptide-coding genes in Medicago truncatula. Five out of fifteen GLV/RGF genes were notably upregulated during nodule organogenesis and were differentially responsive to nitrogen deficiency and auxin treatment. Specifically, the expression of MtGLV9 and MtGLV10 at nodule initiation sites was contingent upon the NODULE INCEPTION transcription factor. Overexpression of these five nodule-induced GLV genes in hairy roots of M. truncatula and application of their synthetic peptide analogues led to a decrease in nodule count by 25-50%. Uniquely, the GOLVEN10 peptide altered the positioning of the first formed lateral root and nodule on the primary root axis, an observation we term 'noduletaxis'; this decreased the length of the lateral organ formation zone on roots. Histological section of roots treated with synthetic GOLVEN10 peptide revealed an increased cell number within the root cortical cell layers without a corresponding increase in cell length, leading to an elongation of the root likely introducing a spatiotemporal delay in organ formation. At the transcription level, the GOLVEN10 peptide suppressed expression of microtubule-related genes and exerted its effects by changing expression of a large subset of Auxin responsive genes. These findings advance our understanding of the molecular mechanisms by which GOLVEN peptides modulate root morphology, nodule ontogeny, and interactions with key transcriptional pathways.

Staufen1 Represses the FOXA1-Regulated Transcriptome by Destabilizing FOXA1 mRNA in Colorectal Cancer Cells.

Transcription factors play key roles in development and disease by controlling gene expression. Forkhead box A1 (FOXA1), is a pioneer transcription factor essential for mouse development and functions as an oncogene in prostate and breast cancer. In colorectal cancer (CRC), FOXA1 is significantly downregulated and high FOXA1 expression is associated with better prognosis, suggesting potential tumor suppressive functions. We therefore investigated the regulation of FOXA1 expression in CRC, focusing on well-differentiated CRC cells, where FOXA1 is robustly expressed. Genome-wide RNA stability assays identified FOXA1 as an unstable mRNA in CRC cells. We validated FOXA1 mRNA instability in multiple CRC cell lines and in patient-derived CRC organoids, and found that the FOXA1 3'UTR confers instability to the FOXA1 transcript. RNA pulldowns and mass spectrometry identified Staufen1 (STAU1) as a potential regulator of FOXA1 mRNA. Indeed, STAU1 knockdown resulted in increased FOXA1 mRNA and protein expression due to increased FOXA1 mRNA stability. Consistent with these data, RNA-seq following STAU1 knockdown in CRC cells revealed that FOXA1 targets were upregulated upon STAU1 knockdown. Collectively, this study uncovers a molecular mechanism by which FOXA1 is regulated in CRC cells and provides insights into our understanding of the complex mechanisms of gene regulation in cancer.

ETS1, a target gene of the EWSR1::FLI1 fusion oncoprotein, regulates the expression of the focal adhesion protein TENSIN3.

The mechanistic basis for the metastasis of Ewing sarcomas remains poorly understood, as these tumors harbor few mutations beyond the chromosomal translocation that initiates the disease. Instead, the epigenome of Ewing sarcoma (EWS) cells reflects the regulatory state of genes associated with the DNA binding activity of the fusion oncoproteins EWSR1::FLI1 or EWSR1::ERG. In this study, we examined the EWSR1::FLI1/ERG's repression of transcription factor genes, concentrating on those that exhibit a broader range of expression in tumors than in EWS cell lines. Focusing on one of these target genes, ETS1, we detected EWSR1::FLI1 binding and an H3K27me3 repressive mark at this locus. Depletion of EWSR1::FLI1 results in ETS1's binding of promoter regions, substantially altering the transcriptome of EWS cells, including the upregulation of the gene encoding TENSIN3 (TNS3), a focal adhesion protein. EWS cell lines expressing ETS1 (CRISPRa) exhibited increased TNS3 expression and enhanced movement compared to control cells. The cytoskeleton of control cells and ETS1-activated EWS cell lines also differed. Specifically, control cells exhibited a distributed vinculin signal and a network-like organization of F-actin. In contrast, ETS1-activated EWS cells showed an accumulation of vinculin and F-actin towards the plasma membrane. Interestingly, the phenotype of ETS1-activated EWS cell lines depleted of TNS3 resembled the phenotype of the control cells. Critically, these findings have clinical relevance as TNS3 expression in EWS tumors positively correlates with that of ETS1.

Comparative Genome Analyses of Plant Rust Pathogen Genomes Reveal a Confluence of Pathogenicity Factors to Quell Host Plant Defense Responses.

Switchgrass rust caused by Puccinia novopanici (P. novopanici) has the ability to significantly affect the biomass yield of switchgrass, an important biofuel crop in the United States. A comparative genome analysis of P. novopanici with rust pathogen genomes infecting monocot cereal crops wheat, barley, oats, maize and sorghum revealed the presence of larger structural variations contributing to their genome sizes. A comparative alignment of the rust pathogen genomes resulted in the identification of collinear and syntenic relationships between P. novopanici and P. sorghi; P. graminis tritici 21-0 (Pgt 21) and P. graminis tritici Ug99 (Pgt Ug99) and between Pgt 21 and P. triticina (Pt). Repeat element analysis indicated a strong presence of retro elements among different Puccinia genomes, contributing to the genome size variation between ~1 and 3%. A comparative look at the enriched protein families of Puccinia spp. revealed a predominant role of restriction of telomere capping proteins (RTC), disulfide isomerases, polysaccharide deacetylases, glycoside hydrolases, superoxide dismutases and multi-copper oxidases (MCOs). All the proteomes of Puccinia spp. share in common a repertoire of 75 secretory and 24 effector proteins, including glycoside hydrolases cellobiohydrolases, peptidyl-propyl isomerases, polysaccharide deacetylases and protein disulfide-isomerases, that remain central to their pathogenicity. Comparison of the predicted effector proteins from Puccinia spp. genomes to the validated proteins from the Pathogen-Host Interactions database (PHI-base) resulted in the identification of validated effector proteins PgtSR1 (PGTG_09586) from P. graminis and Mlp124478 from Melampsora laricis across all the rust pathogen genomes.

PIP-SNP: a pipeline for processing SNP data featured as linkage disequilibrium bin mapping, genotype imputing and marker synthesizing.

Genome-wide association study data analyses often face two significant challenges: (i) high dimensionality of single-nucleotide polymorphism (SNP) genotypes and (ii) imputation of missing values. SNPs are not independent due to physical linkage and natural selection. The correlation of nearby SNPs is known as linkage disequilibrium (LD), which can be used for LD conceptual SNP bin mapping, missing genotype inferencing and SNP dimension reduction. We used a stochastic process to describe the SNP signals and proposed two types of autocorrelations to measure nearby SNPs' information redundancy. Based on the calculated autocorrelation coefficients, we constructed LD bins. We adopted a k-nearest neighbors algorithm (kNN) to impute the missing genotypes. We proposed several novel methods to find the optimal synthetic marker to represent the SNP bin. We also proposed methods to evaluate the information loss or information conservation between using the original genome-wide markers and using dimension-reduced synthetic markers. Our performance assessments on the real-life SNP data from a rice recombinant inbred line (RIL) population and a rice HapMap project show that the new methods produce satisfactory results. We implemented these functional modules in C/C++ and streamlined them into a web-based pipeline named PIP-SNP (https://bioinfo.noble.org/PIP_SNP/) for processing SNP data.

Genome sequence and evolution of Betula platyphylla.

Betula L. (birch) is a pioneer hardwood tree species with ecological, economic, and evolutionary importance in the Northern Hemisphere. We sequenced the Betula platyphylla genome and assembled the sequences into 14 chromosomes. The Betula genome lacks evidence of recent whole-genome duplication and has the same paleoploidy level as Vitis vinifera and Prunus mume. Phylogenetic analysis of lignin pathway genes coupled with tissue-specific expression patterns provided clues for understanding the formation of higher ratios of syringyl to guaiacyl lignin observed in Betula species. Our transcriptome analysis of leaf tissues under a time-series cold stress experiment revealed the presence of the MEKK1-MKK2-MPK4 cascade and six additional mitogen-activated protein kinases that can be linked to a gene regulatory network involving many transcription factors and cold tolerance genes. Our genomic and transcriptome analyses provide insight into the structures, features, and evolution of the B. platyphylla genome. The chromosome-level genome and gene resources of B. platyphylla obtained in this study will facilitate the identification of important and essential genes governing important traits of trees and genetic improvement of B. platyphylla.

LegumeIP V3: from models to crops-an integrative gene discovery platform for translational genomics in legumes.

Legumes have contributed to human health, sustainable food and feed production worldwide for centuries. The study of model legumes has played vital roles in deciphering key genes, pathways, and networks regulating biological mechanisms and agronomic traits. Along with emerging breeding technology such as genome editing, translation of the knowledge gained from model plants to crops is in high demand. The updated database (V3) was redesigned for translational genomics targeting the discovery of novel key genes in less-studied non-model legume crops by referring to the knowledge gained in model legumes. The database contains genomic data for all 22 included species, and transcriptomic data covering thousands of RNA-seq samples mostly from model species. The rich biological data and analytic tools for gene expression and pathway analyses can be used to decipher critical genes, pathways, and networks in model legumes. The integrated comparative genomic functions further facilitate the translation of this knowledge to legume crops. Therefore, the database will be a valuable resource to identify important genes regulating specific biological mechanisms or agronomic traits in the non-model yet economically significant legume crops. LegumeIP V3 is available free to the public at https://plantgrn.noble.org/LegumeIP. Access to the database does not require login, registration, or password.

Insertional mutagenesis of Brachypodium distachyon using the Tnt1 retrotransposable element.

Brachypodium distachyon is an annual C3 grass used as a monocot model system in functional genomics research. Insertional mutagenesis is a powerful tool for both forward and reverse genetics studies. In this study, we explored the possibility of using the tobacco retrotransposon Tnt1 to create a transposon-based insertion mutant population in B. distachyon. We developed transgenic B. distachyon plants expressing Tnt1 (R0) and in the subsequent regenerants (R1) we observed that Tnt1 actively transposed during somatic embryogenesis, generating an average of 6.37 insertions per line in a population of 19 independent R1 regenerant plants analyzed. In seed-derived progeny of R1 plants, Tnt1 segregated in a Mendelian ratio of 3:1 and no new Tnt1 transposition was observed. A total of 126 flanking sequence tags (FSTs) were recovered from the analyzed R0 and R1 lines. Analysis of the FSTs showed a uniform pattern of insertion in all the chromosomes (1-5) without any preference for a particular chromosome region. Considering the average length of a gene transcript to be 3.37 kb, we estimated that 29 613 lines are required to achieve a 90% possibility of tagging a given gene in the B. distachyon genome using the Tnt1-based mutagenesis approach. Our results show the possibility of using Tnt1 to achieve near-saturation mutagenesis in B. distachyon, which will aid in functional genomics studies of other C3 grasses.

The CLE53-SUNN genetic pathway negatively regulates arbuscular mycorrhiza root colonization in Medicago truncatula.

Plants and arbuscular mycorrhizal fungi (AMF) engage in mutually beneficial symbioses based on a reciprocal exchange of nutrients. The beneficial character of the symbiosis is maintained through a mechanism called autoregulation of mycorrhization (AOM). AOM includes root-to-shoot-to-root signaling; however, the molecular details of AOM are poorly understood. AOM shares many features of autoregulation of nodulation (AON) where several genes are known, including the receptor-like kinase SUPER NUMERIC NODULES (SUNN), root-to-shoot mobile CLAVATA3/ENDOSPERM SURROUNDING REGION (ESR)-RELATED (CLE) peptides, and the hydroxyproline O-arabinosyltransferase ROOT DETERMINED NODULATION1 (RDN1) required for post-translational peptide modification. In this work, CLE53 was identified to negatively regulate AMF symbiosis in a SUNN- and RDN1-dependent manner. CLE53 expression was repressed at low phosphorus, while it was induced by AMF colonization and high phosphorus. CLE53 overexpression reduced AMF colonization in a SUNN- and RDN1 dependent manner, while cle53, rdn1, and sunn mutants were more colonized than the wild type. RNA-sequencing identified 700 genes with SUNN-dependent regulation in AMF-colonized plants, providing a resource for future identification of additional AOM genes. Disruption of AOM genes in crops potentially constitutes a novel route for improving AMF-derived phosphorus uptake in agricultural systems with high phosphorus levels.

GPU empowered pipelines for calculating genome-wide kinship matrices with ultra-high dimensional genetic variants and facilitating 1D and 2D GWAS.

Genome-wide association study (GWAS) is a powerful approach that has revolutionized the field of quantitative genetics. Two-dimensional GWAS that accounts for epistatic genetic effects needs to consider the effects of marker pairs, thus quadratic genetic variants, compared to one-dimensional GWAS that accounts for individual genetic variants. Calculating genome-wide kinship matrices in GWAS that account for relationships among individuals represented by ultra-high dimensional genetic variants is computationally challenging. Fortunately, kinship matrix calculation involves pure matrix operations and the algorithms can be parallelized, particular on graphics processing unit (GPU)-empowered high-performance computing (HPC) architectures. We have devised a new method and two pipelines: KMC1D and KMC2D for kinship matrix calculation with high-dimensional genetic variants, respectively, facilitating 1D and 2D GWAS analyses. We first divide the ultra-high-dimensional markers and marker pairs into successive blocks. We then calculate the kinship matrix for each block and merge together the block-wise kinship matrices to form the genome-wide kinship matrix. All the matrix operations have been parallelized using GPU kernels on our NVIDIA GPU-accelerated server platform. The performance analyses show that the calculation speed of KMC1D and KMC2D can be accelerated by 100-400 times over the conventional CPU-based computing.

Identification and Functional Investigation of Genome-Encoded, Small, Secreted Peptides in Plants.

Hundreds to thousands of small secreted peptides (SSPs) are encoded in plant genomes but have been overlooked, and most remain unannotated and unstudied. Despite their low profile, they have been found to confer dramatic effects on growth and development of plants. With the growing appreciation of their significance, the development of appropriate methods to identify and functionally assess the myriad SSPs encoded in plant genomes has become critical. Here, we provide protocols for the computational and physiological analysis of SSPs in plant genomes. We first describe our methodology successfully used for genome-wide identification and annotation of SSP-coding genes in the model legume Medicago truncatula, which can be readily adapted for other plant species. We then provide protocols for the functional analysis of SSPs using various synthetic peptide screens. Considerations for the design and handling of peptides are included. © 2019 by John Wiley & Sons, Inc.

Draft Genome Sequence Resource of Switchgrass Rust Pathogen, Puccinia novopanici Isolate Ard-01.

Puccinia novopanici is an important biotrophic fungal pathogen that causes rust disease in switchgrass. Lack of genomic resources for P. novopanici has hampered the progress toward developing effective disease resistance against this pathogen. Therefore, we have sequenced the whole genome of P. novopanici and generated a framework to understand pathogenicity mechanisms and identify effectors, repeat element invasion, genome evolution, and comparative genomics among Puccinia spp. in the future. Long- and short-read sequences were generated from P. novopanici genomic DNA by PacBio and Illumina technologies, respectively, and assembled a 99.9-Mb genome. Transcripts of P. novopanici were predicted from assembled genome using MAKER and were further validated by RNAseq data. The genome sequence information of P. novopanici will be a valuable resource for researchers working on monocot rusts and plant disease resistance in general.

A microRNA biogenesis-like pathway for producing phased small interfering RNA from a long non-coding RNA in rice.

Phased small interfering RNAs (phasiRNAs) are a class of non-coding RNAs that perform essential functions in plants. Unlike microRNA biogenesis from a hairpin structure, the production of phasiRNAs usually requires a phase initiator and an RNA-dependent RNA polymerase (RDR) to form double-strand RNAs. By using full-length rice cDNA (KL-cDNA) to identify phasiRNA loci, we found that a putative non-coding sequence with a long hairpin structure generates the phasiRNAs, which we name Long Hairpin-structure containing non-coding RNA (LHR). The biogenesis of LHR-derived phasiRNAs was dependent on rice DCL4, but not on RDR2/6, DCL1, or DCL3. Since all of the LHR-phasiRNAs (-5p from the forward strand and -3p from the reverse strand of the dsRNAs) are mapped to the forward strand of LHR, LHR-phasiRNAs should be derived from its hairpin structure, similar to a microRNA precursor. A degradome-based validation suggested that several thylakoid-related genes were targeted by LHR-phasiRNAs. In addition, the production of LHR-phasiRNAs was completely abolished in the lhr mutant, which also exhibited decreased plant height, leaf size, and grain weight, probably through the regulation of photosynthesis. Based on our results, we propose a microRNA biogenesis-like pathway for producing phased siRNAs that expands our understanding of the current model of phased siRNA biogenesis in plants.

GWASpro: a high-performance genome-wide association analysis server.

SUMMARY: We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. AVAILABILITY AND IMPLEMENTATION: GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

2D association and integrative omics analysis in rice provides systems biology view in trait analysis.

The interactions among genes and between genes and environment contribute significantly to the phenotypic variation of complex traits and may be possible explanations for missing heritability. However, to our knowledge no existing tool can address the two kinds of interactions. Here we propose a novel linear mixed model that considers not only the additive effects of biological markers but also the interaction effects of marker pairs. Interaction effect is demonstrated as a 2D association. Based on this linear mixed model, we developed a pipeline, namely PATOWAS. PATOWAS can be used to study transcriptome-wide and metabolome-wide associations in addition to genome-wide associations. Our case analysis with real rice recombinant inbred lines (RILs) at three omics levels demonstrates that 2D association mapping and integrative omics are able to provide a systems biology view into the analyzed traits, leading toward an answer about how genes, transcripts, proteins, and metabolites work together to produce an observable phenotype.

JRmGRN: joint reconstruction of multiple gene regulatory networks with common hub genes using data from multiple tissues or conditions.

Motivation: Joint reconstruction of multiple gene regulatory networks (GRNs) using gene expression data from multiple tissues/conditions is very important for understanding common and tissue/condition-specific regulation. However, there are currently no computational models and methods available for directly constructing such multiple GRNs that not only share some common hub genes but also possess tissue/condition-specific regulatory edges. Results: In this paper, we proposed a new graphic Gaussian model for joint reconstruction of multiple gene regulatory networks (JRmGRN), which highlighted hub genes, using gene expression data from several tissues/conditions. Under the framework of Gaussian graphical model, JRmGRN method constructs the GRNs through maximizing a penalized log likelihood function. We formulated it as a convex optimization problem, and then solved it with an alternating direction method of multipliers (ADMM) algorithm. The performance of JRmGRN was first evaluated with synthetic data and the results showed that JRmGRN outperformed several other methods for reconstruction of GRNs. We also applied our method to real Arabidopsis thaliana RNA-seq data from two light regime conditions in comparison with other methods, and both common hub genes and some conditions-specific hub genes were identified with higher accuracy and precision. Availability and implementation: JRmGRN is available as a R program from: https://github.com/wenpingd. Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-Wide Identification of Medicago Peptides Involved in Macronutrient Responses and Nodulation.

Growing evidence indicates that small, secreted peptides (SSPs) play critical roles in legume growth and development, yet the annotation of SSP-coding genes is far from complete. Systematic reannotation of the Medicago truncatula genome identified 1,970 homologs of established SSP gene families and an additional 2,455 genes that are potentially novel SSPs, previously unreported in the literature. The expression patterns of known and putative SSP genes based on 144 RNA sequencing data sets covering various stages of macronutrient deficiencies and symbiotic interactions with rhizobia and mycorrhiza were investigated. Focusing on those known or suspected to act via receptor-mediated signaling, 240 nutrient-responsive and 365 nodulation-responsive Signaling-SSPs were identified, greatly expanding the number of SSP gene families potentially involved in acclimation to nutrient deficiencies and nodulation. Synthetic peptide applications were shown to alter root growth and nodulation phenotypes, revealing additional regulators of legume nutrient acquisition. Our results constitute a powerful resource enabling further investigations of specific SSP functions via peptide treatment and reverse genetics.

Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach.

Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.